# Machine Learning Foundation

Machine learning teaches computers to do what comes naturally to humans and animals: learn from experience. Machine learning algorithms use computational methods to “learn” information directly from data without relying on a predetermined equation as a model.

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Machine learning uses two types of techniques: supervised learning, which trains a model on known input and output data so that it can predict future outputs, and unsupervised learning,which finds hidden patterns or intrinsic structures in input data. In this course you will learn about different and most popular algorithms of supervised learning.

## Skills you will gain

• ML basics
• Supervised ML
• Linear regression
• KNN
• Data cleaning
• Data Visualization
• Logistic regression
• Naive Bayes Classifier

Module 1

## Machine Learning Foundation

6.0 Hrs

1 Quiz
• Feature or Mathematical space
• Introduction to Supervised machine learning
• Linear regression and it’s Pearson’s coefficient
• Linear regression mathematically and coefficient of Determinant
• Brief scenario of Data set and Descriptive analysis-3
• Analyse the Distribution of dependent column
• Missing Values imputation
• Bivariate analysis using plots through Seaborn function
• Building model using all information
• Cleaning the data, plotting graphs and some mathematical expressions
• Analysis of model and concept of Squared errors
• Concept of fluke correlation
• Logit function in Logistic regression
• Probability examples and model predictions
• Hands-on exercise on logistic regression
• Introduction to Naive Bayes Classifier
• Naive Baye’s Classifier and its example with 2 dimension
• Bayes theorem and formula